Computer Vision and AI in Structural Health Monitoring and Structural Engineering
- 1st Edition - May 1, 2026
- Latest edition
- Authors: Cheng Liu, Yingchao Zhang, Xuebing Xu, Yan Chen
- Language: English
Computer Vision and AI in Structural Health Monitoring and Structural Engineering explores cutting-edge approaches to SHM, integrating advancements in computer vision, artificial… Read more
World Book Day celebration
Where learning shapes lives
Up to 25% off trusted resources that support research, study, and discovery.
Description
Description
Computer Vision and AI in Structural Health Monitoring and Structural Engineering explores cutting-edge approaches to SHM, integrating advancements in computer vision, artificial intelligence (AI), and multimodal technologies to revolutionize how infrastructure is monitored, maintained, and managed. Starting with the fundamentals of SHM and structural engineering, the book examines the transformative power of computer vision applications, such as crack detection, corrosion assessment, and real-time deformation analysis. It also introduces vision-language models (VLMs), enabling automated defect reporting, multimodal analysis, and natural language interfaces for SHM systems.
In an era of aging infrastructure and an increasing demand for safety, structural health monitoring (SHM) has become critical for ensuring the longevity and reliability of buildings, bridges, and other essential structures. This book explores these important concepts.
In an era of aging infrastructure and an increasing demand for safety, structural health monitoring (SHM) has become critical for ensuring the longevity and reliability of buildings, bridges, and other essential structures. This book explores these important concepts.
Key features
Key features
- Provides in-depth coverage on how computer vision and AI technologies transform structural health monitoring (SHM)
- Focuses on the emerging vision-language models that enable automated defect description, multimodal damage assessment, and natural language interfaces for SHM systems, making monitoring processes more intuitive and efficient for users
- Features case studies on bridge monitoring systems, building inspections, and infrastructure maintenance projects, showcasing successful implementations of advanced SHM techniques
- Explores cutting-edge technologies like 5G, edge computing, advanced sensors, and extended reality, highlighting their potential role in the future of SHM and offering readers forward-looking perspectives on the field
Readership
Readership
Academics and researchers: researchers in structural engineering, computer vision, artificial intelligence, and related fields seeking advanced knowledge and innovative approaches to structural health monitoring (SHM)
Table of contents
Table of contents
Part I: Fundamentals
1. Introduction
1.1 Overview of structural health monitoring (SHM)
1.2 Evolution of monitoring techniques
1.3 Current challenges in infrastructure maintenance
2. Basic Concepts
2.1 Structural engineering principles
2.2 Types of structural defects
2.3 Traditional inspection methods
2.4 Digital transformation in construction
Part II: Computer Vision in SHM
3. Computer Vision Fundamentals
3.1 Image processing basics
3.2 Feature detection and extraction
3.3 Object detection and segmentation
3.4 Deep learning architectures for CV
4. CV Applications in Construction
4.1 Crack detection and classification
4.2 Corrosion assessment
4.3 Displacement monitoring
4.4 Real-time structural deformation analysis
Part III: Vision-Language Models
5. Foundation of Vision-Language Models
5.1 Multimodal learning
5.2 Visual transformers
5.3 Large language models in construction
5.4 Cross-modal attention mechanisms
6. VLM Applications
6.1 Defect description and reporting
6.2 Automated inspection documentation
6.3 Natural language interfaces for monitoring
6.4 Multimodal damage assessment
Part IV: Implementation and Evaluation
7. Evaluation Metrics
7.1 Performance metrics for CV systems
7.2 Accuracy and precision measures
7.3 Reliability assessment
7.4 Cost-benefit analysis
8. Data Collection and Management
8.1 Sensor networks and IoT integration
8.2 Data acquisition protocols
8.3 Quality assurance
8.4 Storage and processing infrastructure
Part V: Advanced Topics
9. Automation Systems
9.1 Robotic inspection systems
9.2 Drone-based monitoring
9.3 Edge computing applications
9.4 Real-time monitoring systems
10. AI and Machine Learning
10.1 Predictive maintenance
10.2 Anomaly detection
10.3 Pattern recognition
10.4 Decision support systems
Part VI: Practical Considerations
11. Implementation Guidelines
11.1 System design and architecture
11.2 Integration with existing infrastructure
11.3 Cost considerations
11.4 Training requirements
12. Case Studies
12.1 Bridge monitoring systems
12.2 Building inspection applications
12.3 Infrastructure maintenance projects
12.4 Success stories and lessons learned
Part VII: Future Directions
13. Emerging Technologies
13.1 Advanced sensor technologies
13.2 5G and beyond
13.3 Quantum computing applications
13.4 Extended reality in SHM
14. Research Opportunities
14.1 Current challenges
14.2 Future research directions
14.3 Potential breakthroughs
14.4 Industry trends
1. Introduction
1.1 Overview of structural health monitoring (SHM)
1.2 Evolution of monitoring techniques
1.3 Current challenges in infrastructure maintenance
2. Basic Concepts
2.1 Structural engineering principles
2.2 Types of structural defects
2.3 Traditional inspection methods
2.4 Digital transformation in construction
Part II: Computer Vision in SHM
3. Computer Vision Fundamentals
3.1 Image processing basics
3.2 Feature detection and extraction
3.3 Object detection and segmentation
3.4 Deep learning architectures for CV
4. CV Applications in Construction
4.1 Crack detection and classification
4.2 Corrosion assessment
4.3 Displacement monitoring
4.4 Real-time structural deformation analysis
Part III: Vision-Language Models
5. Foundation of Vision-Language Models
5.1 Multimodal learning
5.2 Visual transformers
5.3 Large language models in construction
5.4 Cross-modal attention mechanisms
6. VLM Applications
6.1 Defect description and reporting
6.2 Automated inspection documentation
6.3 Natural language interfaces for monitoring
6.4 Multimodal damage assessment
Part IV: Implementation and Evaluation
7. Evaluation Metrics
7.1 Performance metrics for CV systems
7.2 Accuracy and precision measures
7.3 Reliability assessment
7.4 Cost-benefit analysis
8. Data Collection and Management
8.1 Sensor networks and IoT integration
8.2 Data acquisition protocols
8.3 Quality assurance
8.4 Storage and processing infrastructure
Part V: Advanced Topics
9. Automation Systems
9.1 Robotic inspection systems
9.2 Drone-based monitoring
9.3 Edge computing applications
9.4 Real-time monitoring systems
10. AI and Machine Learning
10.1 Predictive maintenance
10.2 Anomaly detection
10.3 Pattern recognition
10.4 Decision support systems
Part VI: Practical Considerations
11. Implementation Guidelines
11.1 System design and architecture
11.2 Integration with existing infrastructure
11.3 Cost considerations
11.4 Training requirements
12. Case Studies
12.1 Bridge monitoring systems
12.2 Building inspection applications
12.3 Infrastructure maintenance projects
12.4 Success stories and lessons learned
Part VII: Future Directions
13. Emerging Technologies
13.1 Advanced sensor technologies
13.2 5G and beyond
13.3 Quantum computing applications
13.4 Extended reality in SHM
14. Research Opportunities
14.1 Current challenges
14.2 Future research directions
14.3 Potential breakthroughs
14.4 Industry trends
Product details
Product details
- Edition: 1
- Latest edition
- Published: May 1, 2026
- Language: English
About the authors
About the authors
CL
Cheng Liu
Dr. Liu received his PhD from the Department of Mechanical Engineering at Stanford University and an M.Sc. in Aeronautics and Astronautics, also from Stanford University. Cheng Liu's research is focused on physics-guided machine learning for structural health monitoring (SHM), smart structures, cyber-physical systems/digital twin, robotic tactile sensing and the mechanics of composite structures. His recent research includes the fusion of data-driven and physics-based methods for SHM to improve its robustness and explainability, so that SHM can really be widely applied in real-world scenarios
Affiliations and expertise
City University of Hong Kong, Hong KongYZ
Yingchao Zhang
Yingchao Zhang is currently pursuing a PhD degree in Systems Engineering at the City University of Hong Kong. He received his bachelor's and master’s degrees in civil engineering from Shandong University. His main research interest is in intelligent detection of transport infrastructure
Affiliations and expertise
City University of Hong Kong, Hong KongXX
Xuebing Xu
Xuebing Xu is currently pursuing a PhD degree in Systems Engineering at the City University of Hong Kong. He received his bachelor's and master’s degrees from Huazhong University of Science and Technology. His main research includes the development and application of vision language models and large language models
Affiliations and expertise
City University of Hong Kong, Hong KongYC
Yan Chen
Yan Chen is currently pursuing a PhD degree in Systems Engineering at the City University of Hong Kong. He received his bachelor's from the National University of Defense Technology, China, and a masters degree from the City University of Hong Kong. His main research includes the development and application of deep learning and large language models
Affiliations and expertise
City University of Hong Kong, Hong Kong